PMID- 38222787
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240116
IS  - 1948-9358 (Print)
IS  - 1948-9358 (Electronic)
IS  - 1948-9358 (Linking)
VI  - 14
IP  - 12
DP  - 2023 Dec 15
TI  - Establishment of models to predict factors influencing periodontitis in patients 
      with type 2 diabetes mellitus.
PG  - 1793-1802
LID - 10.4239/wjd.v14.i12.1793 [doi]
AB  - BACKGROUND: Type 2 diabetes mellitus (T2DM) is associated with periodontitis. 
      Currently, there are few studies proposing predictive models for periodontitis in 
      patients with T2DM. AIM: To determine the factors influencing periodontitis in 
      patients with T2DM by constructing logistic regression and random forest models. 
      METHODS: In this a retrospective study, 300 patients with T2DM who were 
      hospitalized at the First People's Hospital of Wenling from January 2022 to June 
      2022 were selected for inclusion, and their data were collected from hospital 
      records. We used logistic regression to analyze factors associated with 
      periodontitis in patients with T2DM, and random forest and logistic regression 
      prediction models were established. The prediction efficiency of the models was 
      compared using the area under the receiver operating characteristic curve (AUC). 
      RESULTS: Of 300 patients with T2DM, 224 had periodontitis, with an incidence of 
      74.67%. Logistic regression analysis showed that age [odds ratio (OR) = 1.047, 
      95% confidence interval (CI): 1.017-1.078], teeth brushing frequency (OR = 4.303, 
      95%CI: 2.154-8.599), education level (OR = 0.528, 95%CI: 0.348-0.800), 
      glycosylated hemoglobin (HbA1c) (OR = 2.545, 95%CI: 1.770-3.661), total 
      cholesterol (TC) (OR = 2.872, 95%CI: 1.725-4.781), and triglyceride (TG) (OR = 
      3.306, 95%CI: 1.019-10.723) influenced the occurrence of periodontitis (P < 
      0.05). The random forest model showed that the most influential variable was 
      HbA1c followed by age, TC, TG, education level, brushing frequency, and sex. 
      Comparison of the prediction effects of the two models showed that in the 
      training dataset, the AUC of the random forest model was higher than that of the 
      logistic regression model (AUC = 1.000 vs AUC = 0.851; P < 0.05). In the 
      validation dataset, there was no significant difference in AUC between the random 
      forest and logistic regression models (AUC = 0.946 vs AUC = 0.915; P > 0.05). 
      CONCLUSION: Both random forest and logistic regression models have good 
      predictive value and can accurately predict the risk of periodontitis in patients 
      with T2DM.
CI  - (c)The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights 
      reserved.
FAU - Xu, Hong-Miao
AU  - Xu HM
AD  - Department of Stomatology, The First People's Hospital of Wenling, Taizhou 
      317500, Zhejiang Province, China.
FAU - Shen, Xuan-Jiang
AU  - Shen XJ
AD  - Department of Stomatology, Taizhou Central Hospital (Taizhou University 
      Hospital), Taizhou 318000, Zhejiang Province, China.
FAU - Liu, Jia
AU  - Liu J
AD  - Department of Stomatology, Taizhou Central Hospital (Taizhou University 
      Hospital), Taizhou 318000, Zhejiang Province, China. liujia_861217@163.com.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - World J Diabetes
JT  - World journal of diabetes
JID - 101547524
PMC - PMC10784791
OTO - NOTNLM
OT  - Gingival disease
OT  - Logistic regression
OT  - Periodontitis
OT  - Prediction model
OT  - Random forest model
OT  - Type 2 diabetes mellitus
COIS- Conflict-of-interest statement: All the authors report no relevant conflicts of 
      interest for this article.
EDAT- 2024/01/15 06:42
MHDA- 2024/01/15 06:43
PMCR- 2023/12/15
CRDT- 2024/01/15 04:36
PHST- 2023/08/08 00:00 [received]
PHST- 2023/10/20 00:00 [revised]
PHST- 2023/11/27 00:00 [accepted]
PHST- 2024/01/15 06:43 [medline]
PHST- 2024/01/15 06:42 [pubmed]
PHST- 2024/01/15 04:36 [entrez]
PHST- 2023/12/15 00:00 [pmc-release]
AID - 10.4239/wjd.v14.i12.1793 [doi]
PST - ppublish
SO  - World J Diabetes. 2023 Dec 15;14(12):1793-1802. doi: 10.4239/wjd.v14.i12.1793.